Emoji Sentiment Analysis of User Reviews on Online Applications Using Supervised Machine Learning

Title

Emoji Sentiment Analysis of User Reviews on Online Applications Using Supervised Machine Learning

Creator

Swetha Cordelia A.; Kokatnoor S.A.

Description

Analyzing the sentiment behind emojis can provide valuable insights into the emotional context and user sentiment associated with textual content. To conduct a comparative analysis of diverse supervised machine learning models that can achieve the highest level of accuracy in Emoji Sentiment Analysis is the purpose of this research. Five machine learning models used in this research are K-Nearest Neighbors (KNN), Artificial Neural Network (ANN), Logistic Regression, Naive Bayes, and Random Forest. The experimental process resulted in ANN and KNN models giving an accuracy of 92%. The ANN model shows its proficiency in effectively managing large datasets. ANN also supports fault tolerance. The KNN model refrains from conducting calculations during the training phase and only constructs a model when a query is executed on the dataset. This characteristic makes KNN particularly well-suited for data mining. Both ANN and K-NN excelled in the experimental study due to these distinctive attributes. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

Source

Lecture Notes in Networks and Systems, Vol-922 LNNS, pp. 257-267.

Date

2024-01-01

Publisher

Springer Science and Business Media Deutschland GmbH

Subject

Artificial neural network; K-Nearest neighbors; Logistic regression; Machine learning; Naive bayes; Natural language processing; Random forest; Sentiment analysis; Supervised

Coverage

Swetha Cordelia A., Department of Computer Science and Engineering, School of Engineering and Technology, CHRIST (Deemed to Be University), Karnataka, Bangalore, 560074, India; Kokatnoor S.A., Department of Computer Science and Engineering, School of Engineering and Technology, CHRIST (Deemed to Be University), Karnataka, Bangalore, 560074, India

Rights

Restricted Access

Relation

ISSN: 23673370; ISBN: 978-981970974-8

Format

Online

Language

English

Type

Conference paper

Identifier

https://doi.org/10.1007/978-981-97-0975-5_23

https://www.scopus.com/inward/record.uri?eid=2-s2.0-85197304422&doi=10.1007%2f978-981-97-0975-5_23&partnerID=40&md5=b53045be74522d33924d6a75255134cc